Data Analysis & Processing

This course equips you with the foundational skills for data analysis and processing, the essential building blocks for leveraging Artificial Intelligence (AI) in Supply Chain Management (SCM). You'll gain hands-on experience wrangling, cleaning, and preparing your data for powerful AI applications, unlocking valuable insights for optimizing your supply chains.

• Understand the importance of data analysis and processing for AI applications in SCM.

• Explore the various stages of the data analysis lifecycle (data collection, cleaning, transformation, analysis).

• Gain hands-on experience with popular data analysis tools and techniques (e.g., Python libraries like Pandas).

• Identify and address common data quality issues (missing values, outliers) relevant to SCM data.

• Prepare real-world SCM data for use in AI models (e.g., demand forecasting, inventory optimization).

1. Foundations of Data Analysis for AI

• Introduction to Data Analysis and AI in SCM: Understanding the role of clean data in building effective AI models.

• The Data Analysis Lifecycle: Exploring the different stages of data preparation (collection, cleaning, transformation, analysis).

• Introduction to Data Analysis Tools: Learning the basics of Python libraries like Pandas for data manipulation and analysis.

• Hands-on Exercises: Utilizing Python libraries to explore, clean, and manipulate real-world SCM data (provided datasets).

• Case Studies: Examining how data analysis helps prepare data for AI models in areas like demand forecasting or inventory optimization.

2. Wrangling Real-World SCM Data

• Diving into Data Cleaning Techniques: Addressing common data quality issues like missing values, outliers, and inconsistencies.

• Feature Engineering for AI Models: Transforming raw data into meaningful features for AI algorithms to utilize.

• Introduction to Data Visualization: Creating informative charts and graphs to understand your cleaned and prepared data.

• Hands-on Exercises: Applying data cleaning techniques and feature engineering to prepare real-world SCM data for a chosen AI task (e.g., predicting transportation delays).

• Course Wrap-up: Exploring responsible data practices and potential biases in SCM data.

• Basic understanding of mathematics and statistics

• Familiarity with programming concepts and a language such as Python or R

• Knowledge of database systems and SQL is beneficial but not required